# Code source: Gaël Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
from sklearn.decomposition import PCA


if __name__ == '__main__':
    # import some data to play with
    iris = datasets.load_iris()
    X = iris.data[:, :2]  # we only take the first two features.
    y = iris.target

    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5

    plt.figure(2, figsize=(8, 6))
    plt.clf()

    # Plot the training points
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1,
                edgecolor='k')
    plt.xlabel('Sepal length')
    plt.ylabel('Sepal width')

    plt.xlim(x_min, x_max)
    plt.ylim(y_min, y_max)
    plt.xticks(())
    plt.yticks(())

    # To getter a better understanding of interaction of the dimensions
    # plot the first three PCA dimensions
    fig = plt.figure(1, figsize=(8, 6))
    ax = Axes3D(fig, elev=-150, azim=110)
    X_reduced = PCA(n_components=3).fit_transform(iris.data)
    ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=y,
               cmap=plt.cm.Set1, edgecolor='k', s=40)
    ax.set_title("First three PCA directions")
    ax.set_xlabel("1st eigenvector")
    ax.w_xaxis.set_ticklabels([])
    ax.set_ylabel("2nd eigenvector")
    ax.w_yaxis.set_ticklabels([])
    ax.set_zlabel("3rd eigenvector")
    ax.w_zaxis.set_ticklabels([])

    plt.show()
